商業智慧實務 practices of business intelligence

商業智慧實務Practices of Business Intelligence

1

1022BI04MI4

Wed, 9,10 (16:10-18:00) (B113)

資料倉儲 (Data Warehousing)

Min-Yuh Day戴敏育

Assistant Professor專任助理教授

Dept. of Information Management, Tamkang University淡江大學資訊管理學系

http://mail. tku.edu.tw/myday/2014-03-12

Tamkang University

http://mail.tku.edu.tw/myday/

http://mail.tku.edu.tw/myday/cindex.htm

http://www.im.tku.edu.tw/en_index.html

http://english.tku.edu.tw/index.asp

http://www.tku.edu.tw/

http://www.im.tku.edu.tw/

http://mail.tku.edu.tw/myday/

週次 (Week) 日期 (Date) 內容 (Subject/Topics)1 103/02/19 商業智慧導論 (Introduction to Business Intelligence)2 103/02/26 管理決策支援系統與商業智慧

(Management Decision Support System and Business Intelligence)

3 103/03/05 企業績效管理 (Business Performance Management)4 103/03/12 資料倉儲 (Data Warehousing)5 103/03/19 商業智慧的資料探勘 (Data Mining for Business Intelligence)

6 103/03/26 商業智慧的資料探勘 (Data Mining for Business Intelligence)

7 103/04/02 教學行政觀摩日 (Off-campus study)8 103/04/09 資料科學與巨量資料分析

(Data Science and Big Data Analytics)

課程大綱 (Syllabus)

2

週次日期內容（ Subject/Topics ）9 103/04/16 期中報告 (Midterm Project Presentation)10 103/04/23 期中考試週 (Midterm Exam)11 103/04/30 文字探勘與網路探勘 (Text and Web Mining)12 103/05/07 意見探勘與情感分析

(Opinion Mining and Sentiment Analysis)

13 103/05/14 社會網路分析 (Social Network Analysis)14 103/05/21 期末報告 (Final Project Presentation)15 103/05/28 畢業考試週 (Final Exam)

課程大綱 (Syllabus)

3

A High-Level Architecture of BI

Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 4

Decision Support and Business Intelligence Systems

(9th Ed., Prentice Hall)

Chapter 8:Data Warehousing


Learning Objectives


• Definitions and concepts of data warehouses• Types of data warehousing architectures• Processes used in developing and managing data

warehouses• Data warehousing operations• Role of data warehouses in decision support• Data integration and the extraction,

transformation, and load (ETL) processes• Data warehouse administration and security

issues

Main Data Warehousing (DW) Topics

• DW definitions• Characteristics of DW• Data Marts • ODS, EDW, Metadata• DW Framework• DW Architecture & ETL Process• DW Development• DW Issues


Data Warehouse Defined• A physical repository where relational data are

specially organized to provide enterprise-wide, cleansed data in a standardized format

• “The data warehouse is a collection of integrated, subject-oriented databases design to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”


Characteristics of DW• Subject oriented• Integrated• Time-variant (time series)• Nonvolatile• Summarized• Not normalized• Metadata• Web based, relational/multi-dimensional • Client/server• Real-time and/or right-time (active)


Data MartA departmental data warehouse that stores only relevant data

– Dependent data mart A subset that is created directly from a data warehouse

– Independent data martA small data warehouse designed for a strategic business unit or a department


Data Warehousing Definitions• Operational data stores (ODS)

A type of database often used as an interim area for a data warehouse

• Oper marts An operational data mart.

• Enterprise data warehouse (EDW)A data warehouse for the enterprise.

• Metadata Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use


A Conceptual Framework for DW

DataSources

ERP

Legacy

POS

OtherOLTP/wEB

External data

Select

Transform

Extract

Integrate

Load

ETL Process

EnterpriseData warehouse

Metadata

Replication

A P

I

/ M

idd

lew

are Data/text

mining

Custom builtapplications

OLAP,Dashboard,Web

RoutineBusinessReporting

Applications(Visualization)

Data mart(Engineering)

Data mart(Marketing)

Data mart(Finance)

Data mart(...)

Access

No data marts option


Generic DW Architectures

• Three-tier architecture1. Data acquisition software (back-end)2. The data warehouse that contains the data & software3. Client (front-end) software that allows users to access

and analyze data from the warehouse• Two-tier architecture

First 2 tiers in three-tier architecture is combined into one

… sometime there is only one tier?


Generic DW Architectures

Tier 2:Application server

Tier 1:Client workstation

Tier 3:Database server

Tier 1:Client workstation

Tier 2:Application & database server


DW Architecture Considerations

• Issues to consider when deciding which architecture to use:– Which database management system (DBMS) should

be used? – Will parallel processing and/or partitioning be used? – Will data migration tools be used to load the data

warehouse?– What tools will be used to support data retrieval and

analysis?


A Web-based DW Architecture

WebServer

Client(Web browser)

ApplicationServer

Datawarehouse

Web pages

Internet/Intranet/Extranet


Alternative DW Architectures

SourceSystems

Staging Area

Independent data marts(atomic/summarized data)

End user access and applications

ETL

(a) Independent Data Marts Architecture

SourceSystems

Staging Area


ETL

Dimensionalized data marts linked by conformed dimentions

(atomic/summarized data)

(b) Data Mart Bus Architecture with Linked Dimensional Datamarts



SourceSystems

Staging Area

Normalized relational warehouse (atomic/some

summarized data)


ETL

(d) Centralized Data Warehouse Architecture

SourceSystems

Staging Area


ETL

Normalized relational warehouse (atomic data)

Dependent data marts(summarized/some atomic data)

(c) Hub and Spoke Architecture (Corporate Information Factory)




Logical/physical integration of common data elements

Existing data warehousesData marts and legacy systmes

Data mapping / metadata

(e) Federated Architecture


Which Architecture is the Best?• Bill Inmon versus Ralph Kimball• Enterprise DW versus Data Marts approach

Empirical study by Ariyachandra and Watson (2006)


Data Warehousing Architectures

1. Information interdependence between organizational units

2. Upper management’s information needs

3. Urgency of need for a data warehouse

4. Nature of end-user tasks5. Constraints on resources

6. Strategic view of the data warehouse prior to implementation

7. Compatibility with existing systems8. Perceived ability of the in-house IT

staff9. Technical issues10. Social/political factors

Ten factors Ten factors thatthat potentially affect the architecture selection decision: potentially affect the architecture selection decision:


Enterprise Data Warehouse(by Teradata Corporation)


Data Integration and the Extraction, Transformation, and Load (ETL)

Process• Data integration

Integration that comprises three major processes: data access, data federation, and change capture.

• Enterprise application integration (EAI)A technology that provides a vehicle for pushing data from source systems into a data warehouse

• Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources

• Service-oriented architecture (SOA)A new way of integrating information systems


Extraction, transformation, and load (ETL) process

Data Integration and the Extraction, Transformation, and Load (ETL) Process

Packaged application

Legacy system

Other internal applications

Transient data source

Extract Transform Cleanse Load

Datawarehouse

Data mart


ETL

• Issues affecting the purchase of and ETL tool– Data transformation tools are expensive– Data transformation tools may have a long learning curve

• Important criteria in selecting an ETL tool– Ability to read from and write to an unlimited number of

data sources/architectures– Automatic capturing and delivery of metadata– A history of conforming to open standards– An easy-to-use interface for the developer and the

functional user


Benefits of DW• Direct benefits of a data warehouse

– Allows end users to perform extensive analysis – Allows a consolidated view of corporate data – Better and more timely information– Enhanced system performance – Simplification of data access

• Indirect benefits of data warehouse– Enhance business knowledge– Present competitive advantage– Enhance customer service and satisfaction– Facilitate decision making– Help in reforming business processes


Data Warehouse Development

• Data warehouse development approaches– Inmon Model: EDW approach (top-down) – Kimball Model: Data mart approach (bottom-up)– Which model is best?

• There is no one-size-fits-all strategy to DW

– One alternative is the hosted warehouse

• Data warehouse structure: – The Star Schema vs. Relational

• Real-time data warehousing?


DW Development Approaches(Kimball Approach) (Inmon Approach)


DW Structure: Star Schema(a.k.a. Dimensional Modeling)

Claim Information

Driver Automotive

TimeLocation

Start Schema Example for an

Automobile Insurance Data Warehouse

Dimensions:How data will be sliced/diced (e.g., by location, time period, type of automobile or driver)

Facts:Central table that contains (usually summarized) information; also contains foreign keys to access each dimension table.


Dimensional Modeling

Data cube Data cube

A two-dimensional, three-dimensional, or higher-dimensional object in which each dimension of the data represents a measure of interest -Grain-Drill-down-Slicing


Best Practices for Implementing DW

• The project must fit with corporate strategy• There must be complete buy-in to the project• It is important to manage user expectations• The data warehouse must be built incrementally• Adaptability must be built in from the start• The project must be managed by both IT and business

professionals (a business–supplier relationship must be developed)

• Only load data that have been cleansed/high quality • Do not overlook training requirements• Be politically aware.


Risks in Implementing DW

• No mission or objective• Quality of source data unknown• Skills not in place• Inadequate budget• Lack of supporting software• Source data not understood• Weak sponsor• Users not computer literate• Political problems or turf wars• Unrealistic user expectations

(Continued …)


Risks in Implementing DW – Cont.

• Architectural and design risks• Scope creep and changing requirements• Vendors out of control• Multiple platforms• Key people leaving the project• Loss of the sponsor• Too much new technology• Having to fix an operational system• Geographically distributed environment• Team geography and language culture


Things to Avoid for Successful Implementation of DW

• Starting with the wrong sponsorship chain• Setting expectations that you cannot meet• Engaging in politically naive behavior• Loading the warehouse with information just

because it is available• Believing that data warehousing database design is

the same as transactional DB design• Choosing a data warehouse manager who is

technology oriented rather than user oriented


Real-time DW(a.k.a. Active Data Warehousing)

• Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly– Push vs. Pull (of data)

• Concerns about real-time BI– Not all data should be updated continuously– Mismatch of reports generated minutes apart– May be cost prohibitive– May also be infeasible


Evolution of DSS & DW


Active Data WarehousingActive Data Warehousing(by Teradata Corporation)(by Teradata Corporation)


Comparing Traditional and Active DW


Data Warehouse Administration

• Due to its huge size and its intrinsic nature, a DW requires especially strong monitoring in order to sustain its efficiency, productivity and security.

• The successful administration and management of a data warehouse entails skills and proficiency that go past what is required of a traditional database administrator.– Requires expertise in high-performance software,

hardware, and networking technologies


DW Scalability and Security• Scalability

– The main issues pertaining to scalability:• The amount of data in the warehouse• How quickly the warehouse is expected to grow• The number of concurrent users• The complexity of user queries

– Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse

• Security– Emphasis on security and privacy


Summary


• Definitions and concepts of data warehouses• Types of data warehousing architectures• Processes used in developing and managing data

warehouses• Data warehousing operations• Role of data warehouses in decision support• Data integration and the extraction,

transformation, and load (ETL) processes• Data warehouse administration and security

issues

References• Efraim Turban, Ramesh Sharda, Dursun Delen,

Decision Support and Business Intelligence Systems, Ninth Edition, 2011, Pearson.

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商業智慧實務 practices of business intelligence

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